Instructions to use jb6692/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jb6692/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="jb6692/model")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("jb6692/model") model = AutoModelForVisualQuestionAnswering.from_pretrained("jb6692/model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea697f8f2f55544bf8f6cf5cbae5ea406ad0e88462ba2d4947e6ae1f52eabb0c
- Size of remote file:
- 4.66 kB
- SHA256:
- be5886755f287519fcb726a75dc1c0526c6ff3ba5497a912ebe138de1a3f6319
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